�
���h�! � � � d dl mZ d dlZd dlZdZdZg ZeD ]:Z ee� � �# e $ r$Z
e� e� de
� �� � Y dZ
[
�3dZ
[
ww xY wer! e dd� e� � z � � �[[[ d dl
mZ n$# e $ rZej Z e d e� d
�� � e�dZ[ww xY wd dlmZmZmZmZmZmZ d dlZd dlmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;m<Z<m=Z=m>Z>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPmQZQmRZRmSZSmTZT d d
lUmVZV d dlWmXZX d dlYmZZZ d dl[m\Z\ d dl]m^Z^m_Z_m`Z`maZambZbmcZcmdZdmeZemfZfmgZgmhZhmiZimjZjmkZk d dlmlZlmmZmmnZnmoZompZpmqZq d dlmrZr d dlsmtZt d dlumvZvmwZwmxZxmyZymzZzm{Z{m|Z|m}Z}m~Z~mZm�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z�m�Z� d dl�m�Z� d dl�m�Z� dZ� d dl�m�Z�m�Z� dZ�nN# e $ rF d dl�m�Z� e�� � Z�e��� de�d � � Z�e��� d� � Z�[�[�Y nw xY wdej� v r ej� d e�d!�"� � [[d#Z�g d$�Z�dS )%� )�annotationsN�restructuredtext)�numpy�pytz�dateutilz: z(Unable to import required dependencies:
�
)�is_numpy_devz
C extension: z� not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext' to build the C extensions first.)�
get_option�
set_option�reset_option�describe_option�option_context�options)8�
ArrowDtype� Int8Dtype�
Int16Dtype�
Int32Dtype�
Int64Dtype�
UInt8Dtype�UInt16Dtype�UInt32Dtype�UInt64Dtype�Float32Dtype�Float64Dtype�CategoricalDtype�PeriodDtype�
IntervalDtype�DatetimeTZDtype�StringDtype�BooleanDtype�NA�isna�isnull�notna�notnull�Index�CategoricalIndex�
RangeIndex�
MultiIndex�
IntervalIndex�TimedeltaIndex�
DatetimeIndex�PeriodIndex�
IndexSlice�NaT�Period�period_range� Timedelta�timedelta_range� Timestamp�
date_range�bdate_range�Interval�interval_range�
DateOffset�
to_numeric�to_datetime�to_timedelta�Flags�Grouper� factorize�unique�value_counts�NamedAgg�array�Categorical�set_eng_float_format�Series� DataFrame)�SparseDtype)�
infer_freq)�offsets)�eval)�concat�lreshape�melt�wide_to_long�merge�
merge_asof�
merge_ordered�crosstab�pivot�pivot_table�get_dummies�from_dummies�cut�qcut)�api�arrays�errors�io�plotting�tseries)�testing)�
show_versions)� ExcelFile�ExcelWriter�
read_excel�read_csv�read_fwf�
read_table�read_pickle� to_pickle�HDFStore�read_hdf�read_sql�read_sql_query�read_sql_table�read_clipboard�read_parquet�read_orc�read_feather�read_gbq� read_html�read_xml� read_json�
read_stata�read_sas� read_spss)�json_normalize)�testF)�__version__�__git_version__T)�get_versionszclosest-tag�versionzfull-revisionid�PANDAS_DATA_MANAGERz�The env variable PANDAS_DATA_MANAGER is set. The data_manager option is deprecated and will be removed in a future version. Only the BlockManager will be available. Unset this environment variable to silence this warning.� )�
stacklevela�
pandas - a powerful data analysis and manipulation library for Python
=====================================================================
**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.
Main Features
-------------
Here are just a few of the things that pandas does well:
- Easy handling of missing data in floating point as well as non-floating
point data.
- Size mutability: columns can be inserted and deleted from DataFrame and
higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned
to a set of labels, or the user can simply ignore the labels and let
`Series`, `DataFrame`, etc. automatically align the data for you in
computations.
- Powerful, flexible group by functionality to perform split-apply-combine
operations on data sets, for both aggregating and transforming data.
- Make it easy to convert ragged, differently-indexed data in other Python
and NumPy data structures into DataFrame objects.
- Intelligent label-based slicing, fancy indexing, and subsetting of large
data sets.
- Intuitive merging and joining data sets.
- Flexible reshaping and pivoting of data sets.
- Hierarchical labeling of axes (possible to have multiple labels per tick).
- Robust IO tools for loading data from flat files (CSV and delimited),
Excel files, databases, and saving/loading data from the ultrafast HDF5
format.
- Time series-specific functionality: date range generation and frequency
conversion, moving window statistics, date shifting and lagging.
)rr r rD r r' |